/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Matrices;
import org.apache.spark.mllib.linalg.Matrix;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.stat.Statistics;
import org.apache.spark.mllib.stat.test.ChiSqTestResult;

import java.util.Arrays;

// $example on$
// $example off$

public class JavaHypothesisTestingExample {
    public static void main(String[] args) {
        
        SparkConf conf = new SparkConf().setAppName("JavaHypothesisTestingExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        
        // $example on$
        // a vector composed of the frequencies of events
        Vector vec = Vectors.dense(0.1, 0.15, 0.2, 0.3, 0.25);
        
        // compute the goodness of fit. If a second vector to demo3 against is not supplied
        // as a parameter, the demo3 runs against a uniform distribution.
        ChiSqTestResult goodnessOfFitTestResult = Statistics.chiSqTest(vec);
        // summary of the demo3 including the p-value, degrees of freedom, demo3 statistic,
        // the method used, and the null hypothesis.
        System.out.println(goodnessOfFitTestResult + "\n");
        
        // Create a contingency matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
        Matrix mat = Matrices.dense(3, 2, new double[]{1.0, 3.0, 5.0, 2.0, 4.0, 6.0});
        
        // conduct Pearson's independence demo3 on the input contingency matrix
        ChiSqTestResult independenceTestResult = Statistics.chiSqTest(mat);
        // summary of the demo3 including the p-value, degrees of freedom...
        System.out.println(independenceTestResult + "\n");
        
        // an RDD of labeled points
        JavaRDD<LabeledPoint> obs = jsc.parallelize(
                Arrays.asList(
                        new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)),
                        new LabeledPoint(1.0, Vectors.dense(1.0, 2.0, 0.0)),
                        new LabeledPoint(-1.0, Vectors.dense(-1.0, 0.0, -0.5))
                )
        );
        
        // The contingency table is constructed from the raw (feature, label) pairs and used to conduct
        // the independence demo3. Returns an array containing the ChiSquaredTestResult for every feature
        // against the label.
        ChiSqTestResult[] featureTestResults = Statistics.chiSqTest(obs.rdd());
        int i = 1;
        for (ChiSqTestResult result : featureTestResults) {
            System.out.println("Column " + i + ":");
            System.out.println(result + "\n");  // summary of the demo3
            i++;
        }
        // $example off$
        
        jsc.stop();
    }
}
